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README.md

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 # Dataset Summary
 
-SWE-rebench is a large-scale dataset designed to support training and evaluation of LLM-based software engineering (SWE) agents. It is constructed using a fully automated pipeline that continuously extracts real-world interactive SWE tasks from GitHub repositories at scale, as detailed in our paper [SWE-rebench: An Automated Pipeline for Task Collection and Decontaminated Evaluation of Software Engineering Agents](https://arxiv.org/abs/2505.20411). The dataset currently comprises over 21,000 issue–pull request pairs from 3,400+ Python repositories, each validated for correctness through automated environment setup and test execution. A curated subset of these tasks also forms the basis of our continuously updated [SWE-rebench leaderboard](https://swe-rebench.com/leaderboard).
+SWE-rebench is a large-scale dataset designed to support training and evaluation of LLM-based software engineering (SWE) agents, building upon and expanding our earlier release, [SWE-bench-extra](https://huggingface.co/datasets/nebius/SWE-bench-extra). It is constructed using a fully automated pipeline that continuously extracts real-world interactive SWE tasks from GitHub repositories at scale, as detailed in our paper [SWE-rebench: An Automated Pipeline for Task Collection and Decontaminated Evaluation of Software Engineering Agents](https://arxiv.org/abs/2505.20411). The dataset currently comprises over 21,000 issue–pull request pairs from 3,400+ Python repositories, each validated for correctness through automated environment setup and test execution. A curated subset of these tasks also forms the basis of our continuously updated [SWE-rebench leaderboard](https://swe-rebench.com/leaderboard).
 SWE-rebench builds upon and extends the methodology of [SWE-bench](https://www.swebench.com/) by incorporating several key enhancements detailed in our paper, including:
 
 * A fully automated pipeline for continuous task collection.